Skip to content
Back to outputs

Predicting hospital mortality for ICU patients: time series analysis

Research output: Contribution to journalArticle

Standard

Predicting hospital mortality for ICU patients: time series analysis. / Awad, Aya; Bader-El-Den, Mohamed; McNicholas, James; Briggs, Jim; El-Sonbaty, Yasser .

In: Health Informatics Journal, 26.07.2019.

Research output: Contribution to journalArticle

Harvard

APA

Vancouver

Author

Bibtex

@article{2592954ec5f64032b912192b07b95bf9,
title = "Predicting hospital mortality for ICU patients: time series analysis",
abstract = "Current mortality prediction models and scoring systems for Intensive Care Unit (ICU) patients are generally usable only after at least 24 or 48 hours of admission as some parameters are unclear at admission. However, some of the most relevant measurements are available shortly following admission. It is hypothesized that outcome prediction may be made using information available in the earliest phase of ICU admission. This study aims to investigate how early hospital mortality can be predicted for ICU patients. We conducted a thorough time-series analysis on the performance of different data mining methods during the first 48 hours of ICU admission. The results showed that the discrimination power of the machine learning classification methods after 6 hours of admission outperformed the main scoring systems used in intensive care medicine (Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score and Sequential Organ Failure Assessment) after 48 hours of admission.",
keywords = "time-series analysis, mortality prediction, missing values, patient mortality, classification, machine learning, critically ill",
author = "Aya Awad and Mohamed Bader-El-Den and James McNicholas and Jim Briggs and Yasser El-Sonbaty",
year = "2019",
month = "7",
day = "26",
doi = "10.1177/1460458219850323",
language = "English",
journal = "Health Informatics Journal",
issn = "1460-4582",
publisher = "SAGE Publications Inc.",

}

RIS

TY - JOUR

T1 - Predicting hospital mortality for ICU patients: time series analysis

AU - Awad, Aya

AU - Bader-El-Den, Mohamed

AU - McNicholas, James

AU - Briggs, Jim

AU - El-Sonbaty, Yasser

PY - 2019/7/26

Y1 - 2019/7/26

N2 - Current mortality prediction models and scoring systems for Intensive Care Unit (ICU) patients are generally usable only after at least 24 or 48 hours of admission as some parameters are unclear at admission. However, some of the most relevant measurements are available shortly following admission. It is hypothesized that outcome prediction may be made using information available in the earliest phase of ICU admission. This study aims to investigate how early hospital mortality can be predicted for ICU patients. We conducted a thorough time-series analysis on the performance of different data mining methods during the first 48 hours of ICU admission. The results showed that the discrimination power of the machine learning classification methods after 6 hours of admission outperformed the main scoring systems used in intensive care medicine (Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score and Sequential Organ Failure Assessment) after 48 hours of admission.

AB - Current mortality prediction models and scoring systems for Intensive Care Unit (ICU) patients are generally usable only after at least 24 or 48 hours of admission as some parameters are unclear at admission. However, some of the most relevant measurements are available shortly following admission. It is hypothesized that outcome prediction may be made using information available in the earliest phase of ICU admission. This study aims to investigate how early hospital mortality can be predicted for ICU patients. We conducted a thorough time-series analysis on the performance of different data mining methods during the first 48 hours of ICU admission. The results showed that the discrimination power of the machine learning classification methods after 6 hours of admission outperformed the main scoring systems used in intensive care medicine (Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score and Sequential Organ Failure Assessment) after 48 hours of admission.

KW - time-series analysis

KW - mortality prediction

KW - missing values

KW - patient mortality

KW - classification

KW - machine learning

KW - critically ill

U2 - 10.1177/1460458219850323

DO - 10.1177/1460458219850323

M3 - Article

JO - Health Informatics Journal

JF - Health Informatics Journal

SN - 1460-4582

ER -

ID: 7156977